System method and computer-accessible medium for determining breast cancer response using a convolutional neural network

Abstract

An exemplary system, method and computer-accessible medium for determining a breast cancer response(s) for a patient(s) can include, for example, receiving an image(s) of an internal portion(s) of a breast of the patient(s), and determining the breast cancer response(s) by applying a neural network(s) to the image(s). The breast cancer response(s) can be a response to at least one chemotherapy treatment. The breast cancer response(s) can include an Oncotype DX recurrence score. The breast cancer response(s) can be a neoadjuvant axillary response. The image(s) can be a magnetic resonance image(s) (MRI). The MRI(s) can include a dynamic contrast enhanced MRI(s).

1 . A non-transitory computer-accessible medium having stored thereon computer-executable instructions for determining at least one breast cancer response for at least one patient, wherein, when a computer arrangement executes the instructions, the computer arrangement is configured to perform procedures comprising:
 receiving at least one image of at least one internal portion of a breast of the at least one patient; and   determining the at least one breast cancer response by applying at least one neural network to the at least one image.   
     
     
         2 . The computer-accessible medium of  claim 1 , wherein the at least one breast cancer response is a response to at least one chemotherapy treatment. 
     
     
         3 . The computer-accessible medium of  claim 1 , wherein the at least one breast cancer response includes an Oncotype DX recurrence score. 
     
     
         4 . The computer-accessible medium of  claim 1 , wherein the at least one breast cancer response is a neoadjuvant axillary response. 
     
     
         5 . The computer-accessible medium of  claim 1 , wherein the at least one image is at least one magnetic resonance image (MRI). 
     
     
         6 . The computer-accessible medium of  claim 5 , wherein the at least one MRI includes at least one dynamic contrast enhanced MRI. 
     
     
         7 . The computer-accessible medium of  claim 1 , wherein the neural network includes a convolutional neural network (CNN). 
     
     
         8 . The computer-accessible medium of  claim 7 , wherein the CNN includes a plurality of layers. 
     
     
         9 . The computer-accessible medium of  claim 8 , wherein the layers include (i) a plurality of combined convolutional and rectified linear unit (ReLu) layers, (ii) a plurality of max pooling layers, (iii) at least one combined fully connected and ReLu layer, and (iv) at least one dropout layer. 
     
     
         10 . The computer-accessible medium of  claim 9 , wherein (i) the combined convolutional and rectified linear unit (ReLu) layers include at least ten combined convolutional and rectified linear unit (ReLu) layers, and (ii) the max pooling layers include at least four max pooling layers. 
     
     
         11 . The computer-accessible medium of  claim 10 , wherein (i) two of the at least ten combined convolutional and rectified linear unit (ReLu) layers have 64×64×64 feature channels, (ii) two of the at least ten combined convolutional and rectified linear unit (ReLu) layers have 32×32×128 feature channels, (iii) three of the at least ten combined convolutional and rectified linear unit (ReLu) layers have 16×16×128 feature channels, and (iv) three of the at least ten combined convolutional and rectified linear unit (ReLu) layers have 8×8×512 feature channels. 
     
     
         12 . The computer-accessible medium of  claim 1 , wherein the computer arrangement is further configured to determine at least one score based on the at least one image using the at least one neural network. 
     
     
         13 . The computer-accessible medium of  claim 12 , wherein the computer arrangement is configured to determine the at least one breast cancer response based on the score. 
     
     
         14 . The computer-accessible medium of  claim 13 , wherein the computer arrangement is configured to determine the at least one breast cancer response based on the score being above 0.5. 
     
     
         15 . The computer-accessible medium of  claim 1 , wherein the computer arrangement is further configured to normalize the at least one image. 
     
     
         16 . The computer-accessible medium of  claim 15 , wherein the computer arrangement is configured to normalize the at least one image by subtracting a mean for a plurality of images of further internal portions of further breasts, and dividing by a standard deviation for the at least one image. 
     
     
         17 . The computer-accessible medium of  claim 1 , wherein the computer arrangement is further configured to (i) translate the at least one image, (ii) rotate the at least one image, (iii) scale the at least one image, and (iv) shear the at least one image. 
     
     
         18 . The computer-accessible medium of  claim 1 , wherein the computer arrangement is further configured segment the at least one image prior to applying the at least one neural network. 
     
     
         19 . A method for determining at least one breast cancer response for at least one patient, comprising:
 receiving at least one image of at least one internal portion of a breast of the at least one patient; and   using a computer arrangement, determining the at least one breast cancer response by applying at least one neural network to the at least one image.   
     
     
         20 - 36 . (canceled) 
     
     
         37 . A system for determining at least one breast cancer response for at least one patient, comprising:
 a computer hardware arrangement configured to:
 receive at least one image of at least one internal portion of a breast of the at least one patient; and 
 determine the at least one breast cancer response by applying at least one neural network to the at least one image. 
   
     
     
         38 - 54 . (canceled)